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Machine learning model helps forecasters improve confidence in storm prediction

Machine learning model helps forecasters improve confidence in storm prediction
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United States , Colorado State University , Aaronj Hill , Andrew Moore , Russ Schumacher , Aaron Hill , Allie Mazurek , American Meteorological Society , Department Of Atmospheric Science , Storm Prediction Center , Storm Prediction , State University , Atmospheric Science , Colorado State Climatologist , Colorado State University Machine Learning , New Paradigm , Medium Range Severe Weather Forecasts , Probabilistic Random Forest , Based Predictions ,

CSU machine learning model helps forecasters

When severe weather is brewing and life-threatening hazards like heavy rain, hail or tornadoes are possible, advance warning and accurate predictions are of utmost importance. Colorado State University weather researchers have given storm forecasters a powerful new tool to improve confidence in their forecasts and potentially save lives.

Over the last several years, Russ Schumacher, professor in the Department of Atmospheric Science and Colorado State Climatologist, has led a team developing a sophisticated machine learning model for advancing skillful prediction of hazardous weather across the continental United States. First trained on historical records of excessive rainfall, the model is now smart enough to make accurate predictions of events like tornadoes and hail four to eight days in advance – the crucial sweet spot for forecasters to get information out to the public so they can prepare. The model is called CSU-MLP, or Colorado State University-Machine Learning Prob ....

United States , Colorado State University , Allie Mazurek , Aaron Hill , Russ Schumacher , American Meteorological Society , Storm Prediction Center , Department Of Atmospheric Science , Method Of Research , State University , Atmospheric Science , Colorado State Climatologist , Colorado State University Machine Learning , New Paradigm , Medium Range Severe Weather Forecasts , Probabilistic Random Forest ,

Machine learning helps pick out stars in a crowd | Udaipur News | Udaipur Latest News | udaipur local news । Udaipur Updates

Machine learning helps pick out stars in a crowd | Udaipur News | Udaipur Latest News | udaipur local news । Udaipur Updates
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Ashutosh Sharma , Monthly Notices Of The Royal Astronomical Society , Department Of Science Technology , European Space Agency , Indian Institute Of Astrophysics , Indian Astronomers , Machine Learning , Milky Way , Indian Institute , Gaia Early Data Release , Probabilistic Random , Gaussian Mixture Model , Probabilistic Random Forest , Ultra Violet Imaging Telescope , Monthly Notices , Royal Astronomical , Artificial Intelligence , Prof Ashutosh Sharma , அசுதோஷ் ஷர்மா , மாதாந்திர அறிவிப்புகள் ஆஃப் தி அரச வானியல் சமூகம் , துறை ஆஃப் அறிவியல் தொழில்நுட்பம் , இந்தியன் நிறுவனம் ஆஃப் வானியற்பியல் , இந்தியன் வானியலாளர்கள் , இயந்திரம் கற்றல் , பால் வழி , இந்தியன் நிறுவனம் ,

Machine learning helps pick out stars in a crowd | Udaipur News | Udaipur Latest News | udaipur local news । Udaipur Updates

Machine learning helps pick out stars in a crowd
Indian Astronomers have developed a new method based on Machine Learning that can identify cluster stars– assembly of stars physically related through common origin, with much greater certainty. The method can be used on clusters of all ages, distances, and densities. The method has been used to identify hundreds of additional stars for six different clusters up to 18000 light-years away and uncover peculiar stars.
Studying stars and how they evolve is the cornerstone of astronomy. But understanding them is difficult since they are observed at different ages. A star cluster is, therefore, a great place to study stars. All stars in a star cluster have approximately the same age and chemistry, so any differences seen can be attributed to the peculiarities in individual stars with certainty. As the clusters are part of the Milky Way, there are many stars between us and the cluster, so it isn’t easy to identify and select th ....

Ashutosh Sharma , Monthly Notices Of The Royal Astronomical Society , Department Of Science Technology , European Space Agency , Indian Institute Of Astrophysics , Indian Astronomers , Machine Learning , Milky Way , Indian Institute , Gaia Early Data Release , Probabilistic Random , Gaussian Mixture Model , Probabilistic Random Forest , Ultra Violet Imaging Telescope , Monthly Notices , Royal Astronomical , Artificial Intelligence , Prof Ashutosh Sharma , அசுதோஷ் ஷர்மா , மாதாந்திர அறிவிப்புகள் ஆஃப் தி அரச வானியல் சமூகம் , துறை ஆஃப் அறிவியல் தொழில்நுட்பம் , இந்தியன் நிறுவனம் ஆஃப் வானியற்பியல் , இந்தியன் வானியலாளர்கள் , இயந்திரம் கற்றல் , பால் வழி , இந்தியன் நிறுவனம் ,

Machine learning helps pick out stars in a crowd

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New Delhi: Indian Astronomers have developed a new method based on Machine Learning that can identify cluster stars– assembly of stars physically related through common origin, with much greater certainty. The method can be used on clusters of all ages, distances, and densities. The method has been used to identify hundreds of additional stars for six different clusters up to 18000 light-years away and uncover peculiar stars.
Studying stars and how they evolve is the cornerstone of astronomy. But understanding them is difficult since they are observed at different ages. A star cluster is, therefore, a great place to study stars. All stars in a star cluster have approximately the same age and chemistry, so any differences seen can be attributed to the peculiarities in individual stars with certainty. As the clusters are part of the Milky Way, there are many stars between us and the cluster, so it isn’t easy to identify and select the stars of a particular cluste ....

New Delhi , Ashutosh Sharma , Monthly Notices Of The Royal Astronomical Society , Department Of Science Technology , European Space Agency , Indian Institute Of Astrophysics , Indian Astronomers , Machine Learning , Milky Way , Indian Institute , Gaia Early Data Release , Probabilistic Random , Gaussian Mixture Model , Probabilistic Random Forest , Ultra Violet Imaging Telescope , Monthly Notices , Royal Astronomical , Artificial Intelligence , Prof Ashutosh Sharma , புதியது டெல்ஹி , அசுதோஷ் ஷர்மா , மாதாந்திர அறிவிப்புகள் ஆஃப் தி அரச வானியல் சமூகம் , துறை ஆஃப் அறிவியல் தொழில்நுட்பம் , இந்தியன் நிறுவனம் ஆஃப் வானியற்பியல் , இந்தியன் வானியலாளர்கள் , இயந்திரம் கற்றல் ,